Import
tokyo_df <- "https://stopcovid19.metro.tokyo.lg.jp/data/130001_tokyo_covid19_patients.csv" %>%
readr::read_csv() %>%
# print() %>% str()
dplyr::mutate(ageBracket = as.factor(`患者_年代`) %>%
forcats::fct_collapse(`不明` = c("-", "不明")) %>%
forcats::fct_relevel("10歳未満", "10代", "20代", "30代",
"40代", "50代", "60代", "70代",
"80代", "90代", "100歳以上",
"不明まはた非公開"),
gender = forcats::as_factor(`患者_性別`)) %>%
dplyr::select(date = `公表_年月日`, pref = `都道府県名`, ageBracket, gender)
tokyo_df
Data wrangling
日別
tokyo_daily <- tokyo_df %>%
dplyr::group_by(date) %>%
dplyr::summarise(n = dplyr::n()) %>%
dplyr::ungroup() %>%
tidyr::complete(
date = seq.Date(from = min(date), to = max(date), by = "day"),
fill = list(n = 0L)
) %>%
dplyr::mutate(
diff = lagdiff(n), # 前日差
cum = cumsum(n), # 累計
ma7 = ma7(n), # 移動平均(7日)
ma28 = ma28(n) # 移動平均(28日)
)
tokyo_daily
年代別
tokyo_ageBracket_daily <- tokyo_df %>%
daily_aggregate(date, ageBracket)
tokyo_ageBracket_daily
Visualize
tokyo_ageBracket_daily %>%
dplyr::filter(date == max(date))





